Movement templates for learning of hitting and batting

By Jens Kober, Katharina Muelling, Oliver Kroemer, Christoph H. Lampert, Bernhard Schölkopf, Jan Peters. 2010

181 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering, Natural Language Processing

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Citations in the corpus

End-to-End Training of Deep Visuomotor Policies (2015, background): “Several recent papers provide surveys of policy search in robotics [3, 14].”
Model-Free Preference-Based Reinforcement Learning (2016, methodology): “In order to keep the computation tractable, we adapt the weighted formulation of a GP given in Kober et al. (2010) to the sparse Gaussian process case given in Snelson and Ghahramani (2006).”
Hierarchical Relative Entropy Policy Search (2014, unknown): “We show that our Hierarchical REPS can learn versatile solutions while also showing an increased performance in terms of learning speed and quality of the found policy in comparison to the nonhierarchical approach.”
A probabilistic approach to robot trajectory generation (2013, background): “Typically, the generalization to new final positions or velocities is based on heuristics and cannot be directly obtained by imitation learning.”
Learning to select and generalize striking movements in robot table tennis (2012, methodology): “Modified hitting primitives The formulation of Kober et al. (2010) with a linear velocity allows us to directly incorporate the desired velocity such that the desired final velocity can be adapted (in addition to the desired initial and final position in Ijspeert’s formulation).”
Learning concurrent motor skills in versatile solution spaces (2012, methodology): “To describe the motion of the robot arm, we use a recent adaption of dynamic motor primitives (DMPs) to hitting movements as presented in [23].”
Model-based contextual policy search for data-efficient generalization of robot skills (2017, methodology): “Additionally, we can also adapt the final desired velocity ġ of the movement with the extension given in [19].”
Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions (2014, methodology): “During the last decade, DS has been used to model discrete motions [6, 7, 29, 30], rhythmic motions [ 31, 32], hitting motions [33, 34, 7], obstacle avoidance [ 35, 36, 37], etc.”
Information-Theoretic Motor Skill Learning (2013, methodology): “For a more detailed description of the DMP framework we refer to (Kober et al. 2010).”
Probabilistic Movement Primitives (2013, background): “Moreover, adaptation to a new task or a new situation requires modulation of the MP to an altered desired target position, target velocity or via-points [3].”
Data-Efficient Generalization of Robot Skills with Contextual Policy Search (2013, methodology): “The results show that CrKR could not compete with model-free REPS.”
A Survey on Policy Search for Robotics (2013, background): “In contrast to exploration in action space, exploration in parameter space is able to use more structured noise and can adapt the variance of the exploration noise in dependence of the state features φt(x).”
Composing Complex Skills by Learning Transition Policies (2018, background): “In the robotics domain, Pastor et al. (2009); Kober et al. (2010); M¨ulling et al. (2013) have proposed a modular approach that learns table tennis by selecting appropriate low-level controllers.”
Imitation Learning (2017, methodology): “This led to a series of work that utilizes the dynamic system in Ijspeert et al. [2002b] to learn from demonstrations and subsequently use reinforcement learning for self-improvement [Kober and Peters 2010, 2014; Buchli et al. 2011; Pastor et al. 2011].”